Team:Paris Liliane Bettencourt

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<span/ id="bottom">[https://2010.igem.org/ iGEM ] > [[Team:Paris_Liliane_Bettencourt#top | Paris]] > [[Team:Paris_Liliane_Bettencourt#top | Home]] > [[Team:Paris_Liliane_Bettencourts#bottom | Synopsis]]
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==Memo-Cell==
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==Summary==
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===Abstract===
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<br>''Counting'' is the action of finding the number of elements in a set.  Past attempts at developing counters in cells have mostly attempted to mimic the binary methods that computers use to count. Our first counter takes a new approach to counting in cells, essentially using a mechanical rotary counter implemented on a micro scale.  Each time the counter detects an input, it performs an excision and an integration directly down-stream of the active site, turning on a reporter and rotating over one "notch" on the counter.
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Our second counter operates on the wholly different principle that the statistical occurrence of a rare event in a large population can be modeled and experimentally verified.  Each cell in our population harbors a construct that when stimulated has a small chance of excising a terminator and expressing a reporter gene which creates cells with a distinctive phenotype.  The number of these cells is thus an accurate count of the number of input stimuli. <br>
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<br>Counting is the action of finding the number of elements in a set. Counting is a basic operation in electronic circuitry accomplished by the use of flip-flops, which are a type of digital device that has two stable states and can act as a single “bit” of memory for a counter.
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<br><a href="https://2010.igem.org/Team:Paris_Liliane_Bettencourt/Project/Memo-cell"><img src="https://static.igem.org/mediawiki/2010/a/aa/Memo_cell-01.jpg" width="129" height="107" title="Memo-Cell"></a><a href="https://2010.igem.org/Team:Paris_Liliane_Bettencourt/Project/Population_counter"><img src="https://static.igem.org/mediawiki/2010/9/93/Pop_counter_logo-01.jpg" width="129" height="107" title="Population Counter"><br></a></center>
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<br>Early efforts by synthetic biologists have shown that implementing this type of counter in cells is not easy.  Since components in a cell are not strictly sequestered in the manner of electronic circuits, and since the cell signalling channels often interfere with each other in ways that are difficult to predict, digital methods seem like a problematic way to count in cells.
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<br>When looking to nature for inspiration, one finds mostly circuits that don’t actually count in a strict sense, but instead act as “threshold detectors” that cause something to happen after a certain threshold has been reached. Cell aggregation, telomere length regulation, and quorum sensing all fall under this category of device.
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As part of the joy of the iGEM competition is actually winning, we have worked out an algorithm based on semantic analysis of past years' wikis that selects and visualises automatically keywords unique to each team. This can be extended in the future to aid in automated analysis of past winners, as well as many other metrics about a given team.
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<br>The first method of counting in our project is capable both of true counting and of threshold detection, and is more similar to the mechanical counters invented in the late 19th century than to any digital circuit.
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<br><br> Last but not least... We made major contributions to the nascent SynBioWorld collaborative web platform that aims at building a universal site for  the synthetic biology community as a place to meet, talk, share data and resources, and stay abreast of new developments in the field. <br><br>
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<br>We have designed a “rotary counter” that counts events within a single cell. Each time the cell detects a pulse of input, it performs an excision and an integration, activating a reporter.  The number of integrated sequences corresponds in a one-to-one fashion with the number of inductions, serving as an accurate counter.  The key to our systems accuracy is a specially designed system that preferentially integrates sequences only to sites directly downstream on the genome, allowing us to integrate in a sequential “rotary” fashion and avoid skips in the count.  Our systems output can be read manually by PCR, or by adding reporter genes directly after any of the integration sites.   
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<a href="https://2010.igem.org/Team:Paris_Liliane_Bettencourt/Project/Synbioworld">
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==Major achievements==
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<br><u>These are our major achievements</u>
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<br>For example, one could integrate a gene that produces a protein of interest to the third recombination site, and a lysis gene to the fifth site. In this way, the system can act as a timer as well as a counter, producing protein for a period of time, and then releasing it to the media at another time.  Since not all the cells in the population will count correctly, there is a fail-safe mechanism that kills any cell that is “off-beat” by using a small intra-cellular toxin.
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* We designed two different types of counter and timer.
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* We managed to go beyond the concept and genetically constructed systems that let us test these devices and make a proof of concept.
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<br>Specifically:
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* <b>Our bacteria count to 2! Nothing stops them from counting more.</b>
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* We have shown that the IntI1 integrase can perform specific excision of a site flanked by two attC sites in response to an arabinose pulse down to 120 minutes.
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* We have shown that our mutated designed IntLambda/IntHK022 system is able to integrate DNA fragments into the chromosome in a sequential way with high efficiency (74%-100%).
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* We designed, cloned and proved the efficiency of the Tn916 transposase.
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* We designed and cloned the smallest bacterial death gene, microcinA (8 amino acids) that serves as a new way of negatively selecting clones.
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<br>* We modeled the population counter that demonstrates the feasibility of our counter and timer approach.
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* We fabricated and tested a microfluidic chemostat.
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* We contributed to the birth of the first online community of synthetic biology lead by students (with collaborators from UCSF and PKU teams)
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* We've made the first steps of using automated semantic analysis algorithm to analyse objectively iGEM wikis.
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* We have learned a lot during iGEM
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<br>Our second experiment is based on a wholly different way to count.  By relying on the statistical ability to predict the behavior of a rare event occuring in a large population, and by exploiting cells ability to talk to each-other, we are creating a “population counter.”  Each cell in our population harbors a construct that, after induction, has a small chance of starting the expression of an antibiotic-resistance gene.  The proportion of resistant cells in the population reflects the number of induction pulses in a highly predictable fashion, thus allowing another type of counter. 
 
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<br>This counter, like the first one, can also be used in “timer” mode.  Timer functionality is achieved by co-expressing with the antibiotic gene LuxI, under a promoter of our choice.  When the media concentration of AHL exceeds a threshold (in part determined by the promoter we choose) YFG is expressed.  To be able to count correctly, this system requires careful control of the media, so we have designed a microfluidic device to test this system.
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[https://igem.org/Team.cgi?year=2010&team_name=Paris_Liliane_Bettencourt Team official Page]
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Latest revision as of 03:58, 28 October 2010


Summary


''Counting'' is the action of finding the number of elements in a set. Past attempts at developing counters in cells have mostly attempted to mimic the binary methods that computers use to count. Our first counter takes a new approach to counting in cells, essentially using a mechanical rotary counter implemented on a micro scale. Each time the counter detects an input, it performs an excision and an integration directly down-stream of the active site, turning on a reporter and rotating over one "notch" on the counter.

Our second counter operates on the wholly different principle that the statistical occurrence of a rare event in a large population can be modeled and experimentally verified. Each cell in our population harbors a construct that when stimulated has a small chance of excising a terminator and expressing a reporter gene which creates cells with a distinctive phenotype. The number of these cells is thus an accurate count of the number of input stimuli.




As part of the joy of the iGEM competition is actually winning, we have worked out an algorithm based on semantic analysis of past years' wikis that selects and visualises automatically keywords unique to each team. This can be extended in the future to aid in automated analysis of past winners, as well as many other metrics about a given team.

Last but not least... We made major contributions to the nascent SynBioWorld collaborative web platform that aims at building a universal site for the synthetic biology community as a place to meet, talk, share data and resources, and stay abreast of new developments in the field.


Major achievements


These are our major achievements

  • We designed two different types of counter and timer.
  • We managed to go beyond the concept and genetically constructed systems that let us test these devices and make a proof of concept.


Specifically:

  • Our bacteria count to 2! Nothing stops them from counting more.
  • We have shown that the IntI1 integrase can perform specific excision of a site flanked by two attC sites in response to an arabinose pulse down to 120 minutes.
  • We have shown that our mutated designed IntLambda/IntHK022 system is able to integrate DNA fragments into the chromosome in a sequential way with high efficiency (74%-100%).
  • We designed, cloned and proved the efficiency of the Tn916 transposase.
  • We designed and cloned the smallest bacterial death gene, microcinA (8 amino acids) that serves as a new way of negatively selecting clones.


* We modeled the population counter that demonstrates the feasibility of our counter and timer approach.

  • We fabricated and tested a microfluidic chemostat.
  • We contributed to the birth of the first online community of synthetic biology lead by students (with collaborators from UCSF and PKU teams)
  • We've made the first steps of using automated semantic analysis algorithm to analyse objectively iGEM wikis.
  • We have learned a lot during iGEM

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